Upload app.py

app.py

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+import json
+from PIL import Image
+import os
+
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+import tensorflow as tf
+import tensorflow_hub as hub
+from tensorflow.keras.utils import img_to_array, load_img
+
+
+@st.cache(allow_output_mutation=True)
+def load_model(path='models/inceptionV3_model'):
+    """Retrieves the trained model and maps it to the CPU by default,
+    can also specify GPU here."""
+    model = tf.keras.Sequential([hub.KerasLayer(path)])
+    model.build([None, 224, 224, 3])
+    return model
+
+
+@st.cache()
+def load_index_to_label_dict(
+        path: str = 'index_to_class_label.json'
+        ) -> dict:
+    """Retrieves and formats the
+    index to class label
+    lookup dictionary needed to
+    make sense of the predictions.
+    When loaded in, the keys are strings, this also
+    processes those keys to integers."""
+    with open(path, 'r') as f:
+        index_to_class_label_dict = json.load(f)
+    index_to_class_label_dict = {
+        int(k): v for k, v in index_to_class_label_dict.items()}
+    return index_to_class_label_dict
+
+
+def load_files(
+        keys: list,
+        path: str = 'images'
+        ) -> list:
+    """Retrieves files from images folder"""
+    files = []
+    for file in os.listdir(path):
+        files.append(Image.open(path + '/' + file))
+    return files
+
+
+def load_image(
+        filename: str = '0.jpg',
+        path: str = 'images'
+        ) -> list:
+    """return image with path and filename"""
+    return Image.open(path + '/' + filename)
+
+
+@st.cache(ttl=24*3600)
+def predict(
+        img: Image.Image,
+        index_to_label_dict: dict,
+        model,
+        k: int
+        ) -> list:
+    """
+    This function transforms the image accordingly,
+    puts it to the necessary device (cpu by default here),
+    feeds the image through the model getting the output tensor,
+    converts that output tensor to probabilities using Softmax,
+    and then extracts and formats the top k predictions.
+    """
+    img = img.resize((224, 224))
+    image = img_to_array(img)  
+    image = np.expand_dims(image, axis=0)
+    preds = model.predict(image)
+    formatted_predictions = list()
+    for idx, x in zip(range(0,6), preds[0]):
+        formatted_predictions.append([index_to_class_label_dict[idx], np.round(x*100,3)])
+    formatted_predictions = sorted(formatted_predictions, key=lambda x:(x[1]), reverse=True)
+    return formatted_predictions
+
+
+if __name__ == '__main__':
+    model = load_model()
+    index_to_class_label_dict = load_index_to_label_dict()
+
+    st.title('Rate My Photo!')
+    instructions = """
+        Either upload your own image or select from
+        the sidebar to get a preconfigured image.
+        The image you select or upload will be fed
+        through the Deep Neural Network in real-time
+        and the output will be displayed to the screen.
+        """
+    st.write(instructions)
+
+    file = st.file_uploader('Upload An Image')
+    images_type = {
+        'Bright': '0.jpg',
+        'Dark': '1.jpg',
+        'Good': '2.jpg',
+        'Lens Flare': '3.jpg',
+        'Loss': '4.jpg',
+        'Motion Blur': '5.jpg'
+    }
+    data_split_names = list(images_type.keys())
+
+    if file:  # if user uploaded file
+        img = Image.open(file)
+        prediction = predict(img, index_to_class_label_dict, model, k=5)
+
+    else:
+        image_type = st.sidebar.selectbox("Examples", data_split_names)
+        image_file = images_type[image_type]
+
+        example_images = load_files(keys=images_type)
+        img = load_image(image_file)
+        prediction = predict(img, index_to_class_label_dict, model, 5)
+
+    st.title("Here is the image you've selected")
+    resized_image = img.resize((336, 336))
+    st.image(resized_image)
+    if prediction[0][0] == "Good":
+        text = "good"
+    else:
+        text = "bad"
+    st.title("Your photo is in " + text + " quality")
+    st.title("Here are the five most likely reasons why")
+    df = pd.DataFrame(data=np.zeros((5, 2)),
+                      columns=['Reason', 'Confidence Level'],
+                      index=np.linspace(1, 5, 5, dtype=int))
+
+    for idx, p in enumerate(prediction[:5]):
+        df.iloc[idx, 0] = p[0]
+        df.iloc[idx, 1] = str(p[1]) + '%'
+    st.write(df.to_html(escape=False), unsafe_allow_html=True)
+    st.write('\n')
+    credits = """
+        This project was developed by <a href="https://www.linkedin.com/in/eran-perelman/" style="text-decoration: none">Eran Perelman</a>, <a href="https://www.linkedin.com/in/asi-sheratzki/" style="text-decoration: none">Asi Sheratzki</a> and <a href="https://www.linkedin.com/in/arykorenvais/" style="text-decoration: none">Ary Korenvais</a> with the guidance of <a href="https://www.linkedin.com/in/morris-alper/" style="text-decoration: none">Morris Alper</a>.
+        """
+    st.write(credits, unsafe_allow_html=True)